This dataset provides two layers of structured match data from the 2024 Wimbledon Championships:

*****wimbledon_2024.csv***** – Point-by-point dataset

Contains detailed stats for every point, including: Player names, serve speed, ace/double fault Point outcome, rally duration, net play, break points Momentum indicators and full match timeline granularity with 48,000+ rows.

*****wimbledon_2024_match_level.csv***** –**Aggregated match-level statistics**.

Clean summary per match with: Total games won, unforced errors, rally time Serve win %, net point win %, match outcome and many more.

Wimbledon 2024 EDA : (https://www.kaggle.com/code/rewantbhriguvanshi/wimbledon-2024-eda)

Potential Use Cases: Sports analytics Tennis match prediction modeling Serve strategy and performance analysis Time-series breakdown of elite tennis matches

Source Credit: Base data originally sourced from (https://github.com/JeffSackmann/tennis_slam_pointbypointl). All credit to the original data provider. This version includes formatting, cleaning, and aggregation for public analysis and research use.

Note: All calculated percentages are based on valid serve attempts; NaNs indicate insufficient data to compute ratios.

Match Group, a market leader in online dating platforms, has experienced a shift in its user base over the past two years. In the first quarter of 2025, the company reported approximately 14.2 million paid users. Despite a slight decline in paid users, Match Group's revenue has continued to grow. Regional distribution of paid subscribers Match Group's paid subscriber base is not evenly distributed across global regions. As of the second quarter of 2024, the Americas led with nearly seven million paying subscribers, followed by Europe with 4.4 million, and the Asia Pacific region with 3.6 million. This regional breakdown provides insight into where Match Group's services, including popular platforms like Tinder, Match.com, and OkCupid, are most widely adopted and monetized. Tinder's dominance in the dating app market Tinder, Match Group's flagship app, continues to dominate the dating app market. In November 2024, Tinder generated over 8.2 million downloads globally across the Apple App Store and Google Play Store. The app's popularity is further evidenced by its financial performance, as it was the highest-grossing dating app worldwide in 2024, generating approximately one billion U.S. dollars in revenue. This success underscores Tinder's significant contribution to Match Group's overall performance and its ability to monetize its user base effectively.

📂 About This Dataset This dataset combines detailed player performance statistics from WhoScored with team and player meta-data from Transfermarkt. It covers over 1,500 players from top European leagues and includes metrics such as:

Expected Goals (xG) & xG per 90

Tackles, Interceptions, Key Passes, Assists

Pass Accuracy, Crosses, Long Balls

Total Minutes Played & Formations

Player Age, Height, Positioning

🧩 Use Cases Player Rating Prediction

Team Formation Impact Analysis

Identifying Underrated Players via xG vs. Goals

Clustering Players by Style or Efficiency

Fantasy Football Recommendations

🏗️ Data Sources WhoScored.com: Player match stats, tactical analysis.

Transfermarkt.com: Player bio, team formations.

📊 Features Snapshot 32 Columns

Over 20 numerical performance metrics

Cleaned, ready-to-analyze format

Small number of missing values (mostly in passing stats)

This dataset contains comprehensive information on the teams participating in the UEFA Euro 2024 tournament. It includes details about each team, their group stage placement, FIFA rankings, captains, head coaches, pre-tournament forms, and average player age.

Columns Description:

• FIFA Ranking: Pre-tournament FIFA ranking of the team
• Team: Name of the team
• Total Points: Points in the pre-tournament FIFA ranking
• Previous Points: Points in the previous FIFA ranking
• Change in Points: Difference between points in Total Points and Previous Points
• Base Camp: Location for training and residency for the duration of UEFA Euro 2024
• Training Ground: Home training ground for the duration of UEFA Euro 2024
• Qualified as: Status under which the team has qualified for UEFA Euro 2024
• Previous appearances: Number of times the team has qualified for previous editions of UEFA Euro Championship
• Manager Name: Name of the manager of the team
• Installation (in years): Number of years the current manager has been coaching the team (rounded down an integer number of years)
• Group: Group in UEFA Euro 2024
• Average Age: Average age of the squad
• Captain: Captain of the team
• Recent Form: Record of wins, draws and losses in the 10 most recent matches played by the team (Friendlies included)

Context

The 2024 ICC Men's T20 World Cup was the ninth edition of the ICC Men's T20 World Cup. It was co-hosted by the West Indies and the United States from 1 to 29 June 2024; the tournament was hosted by the West Indies for the second time, while this was also the first major ICC tournament to feature matches played in the United States.

The tournament field expanded from 16 to 20 teams, including the two hosts, the top eight teams from the 2022 edition, the best-placed two teams in the ICC Men's T20I Team Rankings not already qualified, and eight other teams determined by regional qualifiers. Canada and Uganda qualified for the men's T20 World Cup for the first time, while the United States participated for the first time by being co-hosts.

England were the defending champions and were beaten by India in the semi-finals, who went on to win their second T20 World Cup title, defeating South Africa by 7 runs and equalling England and West Indies with the most titles in T20 World Cup.

Reference: https://en.wikipedia.org/wiki/2024_ICC_Men%27s_T20_World_Cup

Content

• deliveries.csv -> Containing ball by ball all matches data
• matches.csv -> Details for each match played

Acknowledgements

The source of this dataset is Cricsheet. Cricsheet provides ball and ball data for most cricket tournaments. Matches-level data has been processed using raw info files and converted into a single CSV file.

Inspiration

• Analyze the team's performance
• Build visuals to analyze the tournament results
• Use this dataset to make predictions for upcoming T20I matches

In the second quarter of 2024, Match Group generated over 451 million U.S. dollars in direct revenues from paying users in the Americas, up from 450 million in the previous quarter. Revenues generated by paid users in Europe and Asia Pacific regions also increased slightly. The Match Group, formerly owned by IAC, owns and operates online dating platforms such as Match.com, Tinder, OkCupid, Tinder, PlentyofFish and others.

Scraped player stats per game from Understat from 2014/2015 to 2024/2025 (still in progress) seasons.

This contains more detailed information than the dataset from https://www.kaggle.com/datasets/codytipton/understat-data, which includes the individual player stats per game for the English Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and the Russian Football Premier League. In particular, it contains each player's xG, xGBuildup, goals, and shots per game. Furthermore, it has the events for each shot in the events table, clubs and their stats per season in the clubs table, and each game with who lost, won, shots, possession, probabilities of who wins, ect..

This is for educational purposes in our data science bootcamp project.

lineup_stats

• match_id: the id for the match they played
• goals: number of goals for this match
• own_goals: number of own goals for this match
• shots: number of shots for this match
• xG: players xG for this match
• **time*: total amount of time this player played in this match
• player_id: player id
• team_id: id for the players team
• position: players position in this match (SUB means they were substituted in)
• player: player's name
• h_a: 'h' if they are in the home team and 'a' if they are in the away team
• yellow_card: number of yellow cards for this match
• red_card: number of red cards for this match
• **roster_in*: (there is roster information in another table that I did not get, will update later)
• roster_out: (same as roster_in)
• key_passes: number of key passes for this match
• assists: number of assists for this match
• xA: expected assists for this match
• xGChain: total xG for every possession the player is involved in this match
• xGBuildup: Total xG for every possession the player is involved in without key passes and shots in this match
• positionOrder: ordering in the lineup

general_game_stats

• id: this game id
• fid: not sure what this is
• h_id: home team id
• a_id: away team id
• date: date of this game
• league_id: id for the league
• season: which season which game was for
• h_goals: number of goals for the home team
• a_goals: number of goals for the away team
• team_h: home team name
• team_a: away team name
• h_xg: home xG
• a_xg: away xG
• h_w: home win probability
• h_d: home draw probability
• h_l: home loss probability
• league: league name
• h_shot: number of shots by the home team
• a_shot: number of shots by the away team
• h_shotOnTarget: number of shots on target by the home team
• a_shotOnTarget: number of shots on target by the away team
• h_deep:home team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• a_deep: away team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• h_ppda: home team passes allowed per defensive action in the opposition half.
• a_ppda:away team passes allowed per defensive action in the opposition half.

game_events

• id: id for event
• minute: minute the event happend
• result: result (blocked shot, saved shot, ect..)
• X: x-coordinate where the player took the shot
• Y: y-coordinate where the player took the shot
• xG: the xG for the shot
• player: player's name
• h_a: h for home team or a for away team
• player_id: player's id
• situation: situation where this shot happend (direct free kicks, set piece, open play, ect..)
• season: the match season
• shotType: what type of shot (left foot, right foot, head, ect..)
• ** match_id**: id for the match
• h_team: home team name
• ** a_team**: away team name
• ** h_goals**: number of home goals at this time
• ** a_goals**: number of away goals at this time
• date: date of the match
• ** player_assisted**: player who assisted
• lastAction: the last action before this shot

clubs

• club_id: id for the club
• ** club**: club name
• ** league_id** : league id
• ** league**: league name
• ** season**: which season these stats are from
• ** wins**: number of wins that season
• ** draws**: number of draws that season
• ** losses**: number of losses that season
• ** pts**: number of points for that season
• ** avg_xG**: average xG throughout the season
• ** total_goals**: total amount of goals for this season
• total_goals_cond: total amount of goals conceded this season

Statistics illustrates consumption, production, prices, and trade of Matches in Europe from 2007 to 2024.

In 2024, the net income of the Match Group amounted to 551 million U.S. dollars, a decrease from the previous year's result of 651 million U.S. dollars. The Match Group owns and operates many online dating platforms, including market leader Tinder.

Statistics illustrates consumption, production, prices, and trade of Matches in French Polynesia from 2007 to 2024.

Statistics illustrates consumption, production, prices, and trade of Matches in Brazil from 2007 to 2024.

Match history for Messi in 'El Clásico' in the 2024-2025 season and many other statistics about the greatest rivalry in football history

La Liga 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset provides an in-depth look at the 2023/24 La Liga season, covering various aspects of team and player performances across all matchdays. With over 50 individual CSV files, the dataset includes statistics on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings, among others. Whether you're interested in analyzing top scorers, understanding team strengths, or delving into player-specific contributions, this dataset offers a rich foundation for football analytics enthusiasts and professionals.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights for La Liga 2023/24.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly viewable data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - Proper Attribution: Please attribu...

Update [09/Dec/2024]

Uploaded match data for Brasileirão 2024

Update [07/Dec/2023]

Uploaded match data for Brasileirão 2023

Update [14/Nov/2022]

Uploaded match data for Brasileirão 2022

Update [12/Dec/2021]

Uploaded match data for Brasileirão 2021

General info

This dataset contains the matches from 2003-2024 of the Brazilian Championship A-Series (BCAS). I stress the fact that the dataset is validated, i.e., the matches produce the final ranking ipsis literis The main file is the matches-2003-2024.txt with self-explanatory columns (header). The other files are complementary to this one and the other with official rankings (ranking-2003-2024.txt).

Note for the 2020 Season

All matches starting in January/2021 were modified to January/2020 (and subsequent months in 2021) so my scripts will keep functioning without any other tweaking around. This was necessary because of COVID-19. This is important ONLY for studies where the DATES of the matches do matter.

ResearchGate paper

A more comprehensive study may be accessed on ResearchGate, which used Markov Chains for predicting Top 4 and Bottom 4 teams per season.

I stress the fact that the data has been thoroughly validated against official rankings and all exceptions that have happened during each season (detailed in the paper above, with some useful longitudinal statistics on scores).

All files (e.g. Perl scripts) are in GitHub as well.

Brazilian States

Every team belongs to a state in the federation (totalling 27). In the file I list the team's name followed by its state (after a '/' symbol).

AC: Acre AL: Alagoas AP: Amapá AM: Amazonas BA: Bahia CE: Ceará DF: Distrito Federal ES: Espírito Santo GO: Goiás MA: Maranhão MT: Mato Grosso MS: Mato Grosso do Sul MG: Minas Gerais PA: Pará PB: Paraíba PR: Paraná PE: Pernambuco PI: Piauí RJ: Rio de Janeiro RN: Rio Grande do Norte RS: Rio Grande do Sul RO: Rondônia RR: Roraima SC: Santa Catarina SP: São Paulo SE: Sergipe TO: Tocantins

📦 Dataset Overview This dataset contains clean and structured match-level data for last three consecutive IPL seasons:

✅ IPL 2023 — March 31 to May 28

✅ IPL 2024 — Full season covered

✅ IPL 2025 — Complete and up-to-date, including the latest fixtures

It’s ideal for performing Exploratory Data Analysis (EDA), building ML models, and generating insights into match patterns, team performances, and seasonal trends.

📁 Files Included IPL23dataset.csv → Complete data for IPL 2023

ipl_complete_data_2024.csv → Complete data for IPL 2024

ipl_2025_complete_data.csv → Complete and updated data for IPL 2025

📊 What’s Inside? (Key Features) Each match record contains comprehensive information such as:

Match Number & Date Venue Teams Playing (Team 1 & Team 2) Toss Winner & Decision First and Second Innings Teams Match Winner Player of the Match Score Details: Runs, Wickets, Overs, etc.

🧠 Why This Dataset? This dataset is perfect for:

📌 EDA & Visualization Projects

🤖 Machine Learning Model Training

📈 Seasonal Team Performance Comparisons

📊 Fantasy League & Strategy Insights

📰 Sports Journalism & Commentary

🎯 Target Users Data Scientists & Analysts

Machine Learning Practitioners

Cricket Fans & Fantasy League Enthusiasts

Sports Researchers & Statisticians

🔗 Sources & Acknowledgements Credits to reliable sources:

crickhit.com sportskeeda.com Official IPL Website

🔄 Dataset Updates This dataset will continue to stay updated during and after the IPL 2025 season. Found it helpful? Please give it an upvote 👍 and follow the page to stay notified of future versions!

❤️ Thank You! Feel free to use this dataset for analysis, build interactive dashboards, or train cricket-based AI models. Stay tuned and enjoy IPL 2025 — on and off the field!

In 2024, the operating income of the Match Group amounted to 823 million U.S. dollars, representing a 10 percent decrease from the previous year. The Match Group owns and operates many online dating platforms including global favorite, Tinder.

2019

Match history for Ronaldo in national cups in the 2024-2025 season and many other statistics about the greatest rivalry in football history

This dataset provides an in-depth and comprehensive collection of match-by-match data from the ICC Men's T20 World Cup 2024. The dataset covers a wide range of statistics, including detailed batting and bowling performances, match summaries, and player information. It has been meticulously scraped from ESPNcricinfo, a trusted source for cricket statistics and live match data, using Python libraries such as Selenium and BeautifulSoup. I, Mustafa Sultan, am the primary data extractor and source for this dataset.

The dataset is ideal for cricket analysts, enthusiasts, data scientists, and machine learning practitioners who are looking to dive into the intricate details of the 2024 T20 World Cup and use this data for research, analysis, or prediction models.

Primary Source: This data has been scraped from ESPNcricinfo, and I am the sole extractor and compiler of the data, ensuring its completeness and accuracy for public use.

Use Cases: This dataset opens up several analytical possibilities, including: 1. Performance Analysis: Identify top performers, assess consistency, and discover key players for different roles (batting, bowling, all-rounders). 2. Match Outcome Predictions: Use historical data to predict future match outcomes or individual performances. 3. Data Visualization: Create graphs, charts, and dashboards to visualize key insights from the tournament. 4. Fantasy League Team Selections: Use this data to assist in selecting players for fantasy leagues based on performance trends. 5. Statistical Models: Build machine learning models to forecast player performance, predict match results, or analyze game strategies. By scraping this data and compiling it into a user-friendly format, I hope to provide a valuable resource for the cricket analytics community and anyone interested in diving deeper into the statistics of the T20 World Cup 2024.

Statistics illustrates consumption, production, prices, and trade of Matches in Israel from 2007 to 2024.